This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ===== Autonomous AI Agents in Retail Media: Open Systems and Real-World Impact ===== * **Speaker**: Ameya Gokhale * **Room**: HC 203 * **Time**: Sun 4:00 pm – 4:30 pm * **Format**: Lecture (30 Min + Q&A) * **Difficulty**: Some experience required * **Track**: Development & Dev Tools * **Additional Tags**: DevOps, AI / ML * **Presenter Location**: In-person * **Experience**: first time speaking ==== Description: ==== Autonomous AI agents are shifting from assistive tools to **always‑on operators** across online retail, reshaping product discovery, programmatic media buying, and seller operations. This session connects agent capabilities to measurable business outcomes, grounding the discussion in documented industry research and real‑world engineering considerations. The retail AI market is expanding rapidly — from **$4.84B in 2021 to a projected $55.5B by 2030** — driven by personalization, automation, and data‑driven optimization. Studies show that personalized experiences significantly influence consumer behavior: * ~80% of customers are more likely to buy from brands offering personalization * AI‑driven recommendations influence up to **35% of purchases** on major platforms * Conversion lifts of ~15% and average order value increases of up to 20% have been observed In programmatic advertising, autonomous agents deliver: * Up to **44% lower cost per acquisition** * Up to **59% higher conversion rates** * Up to **76% reduction in wasted ad spend** * Stronger performance from AI‑generated and personalized creatives At the operations layer, AI assistants improve catalog management, dynamic pricing, governance, and customer support — reducing manual toil and increasing profit margins. Ameya will emphasize **system architecture**, **automation loops**, and **observability** for Linux‑based production environments, offering engineers a practical understanding of how to build scalable, open, and high‑impact agentic systems. **Target Audience:** * Engineers building production‑grade AI systems * DevOps and platform teams * Retail media practitioners * Anyone interested in open, agentic AI architectures
